Deployment Optimization of Mobile Sensors Based on Genetic Algorithm

Optimal deployment of multiple mobile sensors in three-dimensional space presents a challenging optimization problem. By employing genetic algorithms, we can effectively plan movement trajectories for these sensors to maximize detection effectiveness for target objects.

### Genetic Algorithm Implementation Approach Population Initialization: A population size of 49 is established, where each individual represents a sensor position configuration. Initial solutions are randomly generated to ensure diversity in the search space. Fitness Function: Each individual's detection performance is evaluated using metrics such as coverage area, signal strength, or obstacle avoidance capabilities. This function typically involves calculating spatial coverage using geometric algorithms. Selection Strategy: An elite selection approach is implemented, preserving the best solutions from each generation to prevent loss of high-quality genetic material. Crossover and Mutation: Crossover probability is set at 0.8 to combine advantageous characteristics from different individuals, while mutation probability is maintained at 0.1 to ensure population diversity and prevent premature convergence. Iterative Optimization: Through 1000 generations of evolution, the algorithm gradually approaches the optimal deployment solution using convergence criteria based on fitness improvement thresholds.

### Optimization and Applications This methodology is not limited to sensor deployment but can be extended to scenarios such as UAV formation control and multi-robot coordination systems. By modifying the fitness function parameters, the algorithm can adapt to various optimization objectives including energy consumption minimization or detection accuracy enhancement through appropriate constraint handling mechanisms.